Full Description
Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.
The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack.
This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps.
What You'll Learn
Gain an understanding of the MLOps discipline
Know the MLOps technical stack and its components
Get familiar with the MLOps adoption strategy
Understand feature engineering
Who This Book Is For
Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production
Contents
Chapter 1: Introduction to MLOps.- Chapter 2: MLOps Adoption Strategy and Case Studies.- Chapter 3: Feature Engineering Infrastructure.- Chapter 4: Model Training Infrastructure.- Chapter 5: Model Serving.- Chapter 6: Machine Learning Observability.- Chapter 7: Ray Core.- Chapter 8: Ray Air.- Chapter 9: The Future of MLOps.
